Aim We propose a novel approach that considers taxonomic uniqueness, functional uniqueness and environmental uniqueness and show how it can be used in guiding conservation planning. We illustrate the approach using data for lake biota and environment.

Location Lake Puruvesi, Finland.

Methods We sampled macrophytes and macroinvertebrates from the same 18 littoral sites. By adapting the original “ecological uniqueness” approach, we used distance-based methods to calculate measures of taxonomic (LCBD–t), functional (LCBD–f) and environmental (LCEH) uniqueness for each site. We also considered the numbers and locations of the sites needed to protect up to 70% of total variation in taxonomic, functional or environmental features in the studied part of the lake.

Results Relationships between taxonomic (LCBD–t), functional (LCBD–f) and environmental (LCEH) uniqueness were generally weak, and only the relationship between macrophyte LCBD–t and LCBD–f was statistically significant. Overall, however, if the whole biotic dataset was considered, macroinvertebrate LCBD–f values showed a consistent positive relationship with macrophyte LCBD–f. Depending on the measure of site uniqueness, between one-third to one half of the sites could help protect up to 70% of the ecological uniqueness of the studied part of Lake Puruvesi.

Main conclusions Although the dataset examined originated from a large lake system, the approach we proposed here can be applied in different ecosystems and at various spatial scales. An important consideration is that a set of sites has been sampled using the same methods, resulting in species and environmental matrices that can be analysed using the methodological approach proposed here. This framework can be easily applied to grid-based data, sets of islands or sets of forest fragments. We suggest that the approach based on taxonomic, functional and environmental uniqueness will be a useful tool in guiding nature conservation and ecosystem management, especially if associated with meta-system ideas or network thinking.